Mehmet Balcilar

Investor Herds and Regime-Switching: Evidence from Gulf Arab Stock Markets

This paper proposes a dynamic herding approach which takes into account herding under different market regimes, with concentration on the Gulf Arab stock markets – Abu Dhabi, Dubai, Kuwait, Qatar and Saudi Arabia. Our results support the presence of three market regimes (low, high and extreme or crash volatility) in those markets with the transition order ‘low, crash and high volatility’, suggesting that these frontier markets have a different structure than developed markets. The results also yield evidence of herding behavior under the crash regime for all of the markets except Qatar which herds under the high volatility regime. The findings of the cross-GCC herding model also demonstrate herding comovements and not spillovers and are also robust to the cross-GCC volatility shocks. The tests that underline the cross-volatility shocks suggest that the crash regime is a true regime and not a statistical artifact. Policy and portfolio diversification implications are discussed.

The Causal Relationship Between Economic Policy Uncertainty and Stock Returns in China and India: Evidence from a Bootstrap Rolling Window Approach

Abstract This article applies a bootstrap rolling-window causality test to assess the causal relationship between economic policy uncertainty (EPU) and stock returns in China and India. Empirical literature examining causality between two time series may suffer from inaccurate results when the underlying full-sample time series have structural changes. However, the bootstrap rolling-window approach enables us to identify possible time-varying causalities between time series based on sub-sample data. Using a twenty-four-months rolling window over the period 1995:02 to 2013:02 in China and 2003:02–2013:02 in India, we do find that there are bidirectional causal relationships between EPU and stock returns in several sub-periods rather than in the whole sample period. However, the association between EPU and stock returns is, in general, weak for these two emerging countries. Our findings have important implications for policy makers and investors.

Ripple effects in South African house prices

This paper analyses the ‘ripple’ effect of house prices in large-, medium- and small-sized houses of five major metropolitan areas of South Africa—namely, Cape Town, Durban Unicity, Greater Johannesburg, Port Elizabeth/Uitenhage and Pretoria—based on available quarterly data covering the period of 1966:Q1 to 2010:Q1. Following the extant literature, the issue is contextualised as a unit root problem, with one expecting the ratios of metropolitan house price to national house price to exhibit stationarity to an underlying trend value, if there is diffusion in house prices. Using Bayesian and non-linear unit root tests, besides the standard linear tests of stationarity with and without structural break, overwhelming support is found for the existence of robust ripple effects. Also factor analysis conducted suggested that ripple effects originate in Cape Town for the large housing segment and in Durban for the medium- and small-sized houses.

Effect of Global Shocks and Volatility on Herd Behavior in an Emerging Market: Evidence from Borsa Istanbul

In this article, we examine the dynamic relationship between global factors and herd behavior in an emerging market. Utilizing a time-varying transition probability Markov-switching model, we examine the role of global risk factors on investor behavior in Borsa Istanbul, which is dominated largely by foreign investors. Our tests yield three distinct market regimes (low, high, and extreme volatility) and evidence consistent with herd behavior during both the high- and extreme-volatility regimes. U.S. market–related factors are found to dominate regime transitions and thus significantly contribute to herd behavior in all market sectors with the exception of industrials, suggesting that industrials are relatively immune to global shocks. Multivariate synchronization tests further suggest that herding regimes are perfectly synchronized across all market sectors.

Persistence in Inflation: Does Aggregation Cause Long Memory?

This paper examines persistence in Turkish inflation rates using data from consumer and wholesale price indices. The inflationary process in Turkey is believed to be highly inertial, which should lead to strongly persistent inflation series. Persistence of seventy-five inflation series at various aggregation levels is examined by estimating models that allow long memory through fractional differencing. The order of fractional differencing is estimated using several semiparametric and maximum likelihood methods. Persistence of each series is evaluated using the time required for a given percentage of the effect of a shock to dissipate. We find that disaggregate inflation series show no significant persistence. We found that only twelve out of seventy-five series require more than six months for 99 percent of the effect of a shock to dissipate. Thus, the paper finds evidence of spurious long memory due to aggregation.

Military expenditure, economic growth and structural instability: a case study of South Africa

This paper contributes to the growing literature on the milex-growth nexus, by providing a case study of South Africa and considering the possibility of structural breaks by applying newly developed econometric methods. Using full sample bootstrap Granger non-causality tests, no Granger causal link is found between military expenditure and GDP for 1951–2010, but parameter instability tests show the estimated VARs to be unstable. Using a bootstrap rolling window estimation procedure, however, finds evidence of bidirectional Granger causality in various subsamples. This implies standard Granger non-causality tests, which neither account for structural breaks nor time variation may be invalid.

Causality between Exports and Economic Growth in South Africa: Evidence from Linear and Nonlinear Tests

This paper investigates the dynamic causal link between exports and economic growth using both linear and nonlinear Granger causality tests. We use annual South African data on real exports and real gross domestic product from 1911-2011. The linear Granger causality result shows no evidence of significant causality between exports and GDP. The relevant VAR is unstable, which undermines our confidence in the causality result identified by the linear Granger causality test. Accordingly we turn to the nonlinear methods to evaluate Granger causality between exports and GDP. First, we use Hiemstra and Jones (1994) nonlinear Granger causality test and find a unidirectional causality from GDP to exports. However, using a more powerful and less biased nonlinear test, the Diks and Panchenko (2006) test, we find evidence of significant bi-directional causality. These results highlight the risk of misleading conclusions based on the standard linear Granger causality tests which neither accounts for structural breaks nor uncover nonlinearities in the dynamic relationship between exports and GDP.

Time-varying causality between research output and economic growth in US

This main purpose of this paper is to investigate the causal relationship between knowledge (research output) and economic growth in US over 1981–2011. To overcome the issues of ignoring possible instability and hence, falsely assuming a constant relationship through the years, we use bootstrapped Granger non-causality tests with fixed-size rolling-window to analyze time-varying causal links between two series. Instead of just performing causality tests on the full sample which assumes a single causality relationship, we also perform Granger causality tests on the rolling sub-samples with a fixed-window size. Unlike the full-sample Granger causality test, this method allows us to capture any structural shifts in the model, as well as, the evolution of causal relationships between sub-periods, with the bootstrapping approach controlling for small-sample bias. Full-sample bootstrap causality tests reveal no causal relationship between research and growth in the US. Further, parameter stability tests indicate that there were structural shifts in the relationship, and hence, we cannot entirely rely on full-sample results. The bootstrap rolling-window causality tests show that during the sub-periods of 2003–2005 and 2009, GDP Granger caused research output; while in 2010, the causality ran in the opposite direction. Using a two-state regime switching vector smooth autoregressive model, we find unidirectional Granger causality from research output to GDP in the full sample.

Predicting BRICS stock returns using ARFIMA models

This article examines the existence of long memory in daily stock market returns from Brazil, Russia, India, China and South Africa (BRICS) countries and also attempts to shed light on the efficacy of autoregressive fractionally integrated moving average (ARFIMA) models in predicting stock returns. We present evidence which suggests that ARFIMA models estimated using a variety of estimation procedures yield better forecasting results than the non-ARFIMA (AR, MA, ARMA and GARCH) models with regard to prediction of stock returns. These findings hold consistently for the different countries whose economies differ in size, nature and sophistication.

Housing and the Great Depression

This paper considers the role of the real housing price in the Great Depression. More specifically, we examine structural stability of the relationship between the real housing price and real GDP per capita. We test for structural change in parameter values, using a sample of annual US data from 1890 to 1952. The paper examines the long-run and short-run dynamic relationships between the real housing price and real GDP per capita to determine if these relationships experienced structural change over the sample period. We find that temporal Granger causality exists between these two variables only for sub-samples that include the Great Depression. For the other sub-sample periods as well as for the entire sample period no relationship exists between these variables.